\begin{abstract}
%\begin{quote}

Software bug localization is the problem of determining buggy statements
in a software system. It is a crucial and expensive step in the software
debugging process. Interest in it has grown rapidly in recent years, 
and many approaches have been proposed. However, existing approaches
tend to use isolated information to address the
problem, and are often ad hoc. This paper proposes a well-founded, integrated
solution to the software bug localization problem based on Markov logic. Markov
logic combines first-order logic and probabilistic graphical models by attaching
weights to first-order formulas, and viewing them as templates for features
of Markov networks. We show how a number of salient
 program features can be seamlessly
combined in Markov logic, and how the resulting joint inference
can be solved. 

We implemented our approach in a debugging system, and evaluated it
 on 4 real-world programs. Our
results demonstrated that our approach achieved higher accuracy 
with substantially less engineering effort compared to previous approaches.


%\end{quote}
\end{abstract}

